Author:
Song Dixiang,Zhai Yixuan,Tao Xiaogang,Zhao Chao,Wang Minkai,Wei Xinting
Abstract
AbstractThis study attempts to explore the radiomics-based features of multi-parametric magnetic resonance imaging (MRI) and construct a machine-learning model to predict the blood supply in vestibular schwannoma preoperatively. By retrospectively collecting the preoperative MRI data of patients with vestibular schwannoma, patients were divided into poor and rich blood supply groups according to the intraoperative recording. Patients were divided into training and test cohorts (2:1), randomly. Stable features were retained by intra-group correlation coefficients (ICCs). Four feature selection methods and four classification methods were evaluated to construct favorable radiomics classifiers. The mean area under the curve (AUC) obtained in the test set for different combinations of feature selecting methods and classifiers was calculated separately to compare the performance of the models. Obtain and compare the best combination results with the performance of differentiation through visual observation in clinical diagnosis. 191 patients were included in this study. 3918 stable features were extracted from each patient. Least absolute shrinkage and selection operator (LASSO) and logistic regression model was selected as the optimal combinations after comparing the AUC calculated by models, which predicted the blood supply of vestibular schwannoma by K-Fold cross-validation method with a mean AUC = 0.88 and F1-score = 0.83. Radiomics machine-learning classifiers can accurately predict the blood supply of vestibular schwannoma by preoperative MRI data.
Publisher
Springer Science and Business Media LLC
Reference31 articles.
1. Mahaley, M. et al. Analysis of patterns of care of brain tumor patients in the United States: A study of the Brain Tumor Section of the AANS and the CNS and the Commission on Cancer of the ACS. Clin. Neurosurg. 36, 347–352 (1990).
2. Reznitsky, M. et al. The natural history of Vestibular Schwannoma growth-prospective 40-year data from an unselected national cohort. Neuro Oncol. 23, 827–836 (2020).
3. Halliday, J. et al. An update on the diagnosis and treatment of vestibular schwannoma. Expert Rev. Neurother. 18(1), 29–39 (2018).
4. Alfaifi, A. et al. The top 50 most-cited articles on acoustic neuroma. World Neurosurg. 111, e454–e464 (2018).
5. Yamakami, I. et al. Hypervascular vestibular schwannomas. Surg. Neurol. 57(2), 105–112 (2002).
Cited by
9 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献